关键词: 改良脑小血管病总负荷评分; 急性缺血性卒中; 自发性出血转化; 列线图

Abstract: Objective  To construct and validate a nomogram prediction model based on the modified total burden score of cerebral small vessel disease for the precise prediction of spontaneous hemorrhagic transformation in acute ischemic stroke patients without reperfusion therapy.
Methods   Acute ischemic stroke patients without reperfusion therapy hospitalized in Sixth People’s Hospital of Nanhai District, Foshan from January 2021 to July 2024 were prospectively enrolled. They were randomly divided into a training set and an internal validation set at a ratio of 7∶3. Additionally, patients who met the inclusion and exclusion criteria and were admitted to the same hospital during different time periods (January to December 2020, and August 2024 to October 2025) were recruited as an external validation set. Data including gender, age, past medical history, hematological and imaging examination results were collected for all patients. Imaging markers of cerebral small vessel disease were evaluated based on cranial MRI examination results, and each marker was assigned a value according to its location and severity to calculate the modified total burden score of cerebral small vessel disease. The diagnosis of spontaneous hemorrhagic transformation was determined based on the results of the second cranial CT or MRI examination during hospitalization. With the occurrence of spontaneous hemorrhagic transformation as the dependent variable, univariate and multivariate logistic regression analyses were performed on the training set to screen for predictive factors, which were then used to construct the nomogram prediction model. Calibration curve was used to assess the consistency of the model, ROC curve was applied to evaluate the prediction efficacy of the model, and decision curve analysis and clinical impact curve were used to evaluate the clinical application value of the model.
Results  A total of 1430 acute ischemic stroke patients without reperfusion therapy were included, with a mean age of (67.9±10.4) years, including 716 females (50.1%). The training set comprised 547 patients, with a mean age of (68.2±10.4) years, including 279 females (51.0%). Multivariate logistic regression analysis showed that the modified total burden score of cerebral small vascular disease (OR 2.817, 95%CI 2.210-3.591, P<0.001) and large hemispheric infarction (OR 2.642, 95%CI 1.115-6.260, P=0.027) were independent risk factors for spontaneous hemorrhagic transformation after acute ischemic stroke. The calibration curve of the nomogram prediction model in the training set showed good agreement between the predicted and observed values. The ROC curve showed an AUC of 0.835 (95%CI 0.789-0.880), indicating that the model had good predictive efficacy. The decision curve analysis results revealed that the net benefit was the highest when the threshold probability was from 0.06 to 0.77. The clinical impact curve analysis suggested that the model had an acceptable cost-benefit ratio, indicating high clinical application value. The internal validation set included 235 patients, with a mean age of (68.3±10.4) years, including 119 females (50.6%), and the ROC curve showed an AUC of 0.847 (95%CI 0.785-0.910). The external validation set included 648 patients, with a mean age of (67.4±10.4) years, including 318 females (49.1%), and the ROC curve showed an AUC of 0.870 (95%CI 0.795-0.931).
Conclusions  The nomogram prediction model constructed in this study can effectively predict the risk of spontaneous hemorrhagic transformation after acute ischemic stroke.

Key words: Modified total burden score of cerebral small vessel disease; Acute ischemic stroke; Spontaneous hemorrhagic transformation; Nomogram